Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies
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Computer Science > Machine Learning
Title:Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies
Abstract:Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought experiment to answer a fundamental inquiry: how wide must a neural network be to almost accurately approximate the ACOPF manifold? We introduce a Loss-Guided Neural Densification (LG-ND) algorithm that incrementally discovers necessary capacity by expanding only when the current deep neural network topology fails to improve further. Empirical results across various IEEE systems show that LG-ND achieves performance parity with literature baselines using up to ten times fewer neurons per layer. Such architectural minimalism is critical for the formal verification required in safety-critical grid operations.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.03125 [cs.LG] |
| (or arXiv:2606.03125v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.03125
arXiv-issued DOI via DataCite (pending registration)
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